06sugiyama.dvi
|
|
- ことこ なかじゅく
- 7 years ago
- Views:
Transcription
1 Web Web Web Web Personal Name Disambiguation in Web Search Results Using a Semi-Supervised Clustering Approach Kazunari Sugiyama and Manabu Okumura Personal names are often submitted to search engines as query keywords. However, in response to a personal name query, search engines return a long list of search results that contains Web pages about several namesakes. In order to address this problem, most of the previous works that disambiguate personal names in Web search results often employ agglomerative clustering approaches. In contrast, we have adopted a semi-supervised clustering approach to integrate similar documents into a seed document. Our proposed semi-supervised clustering approach is novel in that it controls the fluctuation of the centroid of a cluster. Key Words: Web information retrieval, Semi-supervised clustering, Personal name disambiguation 1 ALLTheWeb Web, Department of Computer Science, National University of Singapore, Precision and Intelligence Laboratory, Tokyo Institute of Technology
2 Vol. 16 No. 5 October 2009 Google 3 William Cohen Web Web (Mann and Yarowsky 2003) (Pedersen, Purandare, and Kulkarni 2005) (Bekkerman, El-Yaniv, and McCallum 2005) (Bollegala, Matsuo, and Ishizuka 2006) Web seed seed Web (1) (2) Wagstaff (Wagstaff and Cardie 2000) (Wagstaff, Rogers, and Schroedl 2001) K-means must-link 2 cannot-link 2 2 Basu (Basu, Banerjee, and Mooney 2002) K-means Klein (Klein, Kamvar, and Manning 2002) 2 (x i,x j ) 0 2 (max i,j D ij )+1 Xing (Xing, Ng, Jordan, and Russell 2003) Bar-Hillel (Bar-Hillel, Hertz, and Shental 2003) RCA (Relevant Component Analysis) (Shental, Hertz, Weinshall, and Pavel 2002) seed
3 Web K K-means (MacQueen 1967) Web (Wagstaff and Cardie 2000) (Wagstaff et al. 2001) (Basu et al. 2002) (Klein et al. 2002) (Xing et al. 2003) (Bar-Hillel et al. 2003) seed seed (1) seed seed (2) Web seed W p Web p i w pi (i =1,,n) (1) w pi =(w pi t 1,w pi t 2,,w pi t m ) (1) m W p t k (k =1, 2,,m) (a) Term Frequency (TF) (b) Inverse Document Frequency (IDF) (c) residual IDF (RIDF) (d) TF-IDF (e) x I -measure (f) gain 6 25
4 Vol. 16 No. 5 October 2009 (a) Term Frequency (TF) TF tf(t k,p i ) Web p i t k w pi w pi t k (2) tf(t w pi k,p i ) t k = m s=1 tf(t s,p i ) (b) Inverse Document Frequency (IDF) (Jones 1973) IDF w pi w pi t k (3) N w pi t k =log df (t k ) N Web df (t k ) t k Web (c) Residual Inverse Document Frequency (RIDF) Church and Gale (Church and Gale 1995a, 1995b) IDF residual IDF IDF IDF cf k t k N Web 1 Web t k λ k = cf k N wpi w pi t k (4) 1 w pi t k = IDF log 1 p(0; λ i ) N =log df (t k ) +log(1 p(0; λ k)) (4) p λ k RIDF (d) TF-IDF TF-IDF (Salton and McGill 1983) TF-IDF (a) TF (b) IDF (5) w pi t k = tf(t k,p i ) m s=1 tf(t s,p i ) log N df (t k ) (2) (3) (5) 26
5 Web tf(t k,p i ) df (t k ) Web p i t k t k Web N Web (e) x I -measure Bookstein and Swanson (Bookstein and Swanson 1974) t k x I -measure tf(t k,p i ) Web p i t k df (t k ) t k Web w pi w pi t k (6) w pi t k = tf(t k,p i ) df (t k ) (6) 2 (f) gain IDF Papineni (Papineni 2001) IDF gain w pi w pi t k (7) w pi t k = df (t ( k) df (tk ) N N 1 log df (t ) k) (7) N df (t k ) t k Web N Web (a) (f) (f) gain C G C (8) G C =(g C t 1,g C t 2,,g C t m ) (8) gt C k G C t k (k =1, 2,,m) 2 C i C j sim(c i,c j ) (9) G sim(c i,c j )= GCi Cj G Ci G Cj (9) G Ci G Cj C i C j 2.1 Web 27
6 Vol. 16 No. 5 October C i ( n i ) C j ( n j ) C new G new (10) w G new = p C i w p + w p C j w p (10) n i + n j 2.2 seed C sj seed C i C i ( G Ci ) seed C sj ( G C ) D(G Ci, G C ) Web p w p C i k j seed C s (kj) j ( n sj ) C i ( n i ) k j
7 Web C (0) (1) C (kj) seed C i C s (kj) j j G C(k ) C i G Ci D(G Ci, G C(k j ) ) C i Web w p l C i (l =1,,n i ) C i ( n i ) C i Web w p l C i (11) w p l C = w pl C i i D(G Ci, G C(k j ) )+c (11) D(G Ci, G C(k j ) ) (i) (ii) (iii) c D(G Ci, G C(k j ) ) 0 w p c (2) seed C s (kj) j ( n sj ) C i ( n i ) C s (kj+1) j ( n sj + n i ) C (kj+1) = {w p1 C (k j ),, w pnsj C (k j ), w p1 C i,, wpn i C i } (3) k j +1 C (kj+1) G C(k j +1) (12) (11) w p l C i n i G C(k j +1) = w p C (k j +1) n sj + n i 1 w p D(G C i,g C(k j ) )+c (12) seed 2 G new (13) w G new = p C i w p + w p C j w p (13) n i + n j seed seed Web seed 29
8 Vol. 16 No. 5 October 2009 seed (Rocchio 1971) seed Clusty 4 Web seed seed 2 Web seed seed 2 7 else if (11) (i) (ii) (iii) 3 (i) (11) seed G Cs C G C D(G Cs, G C ) (8) (14) D(G Cs, G C )= m (gt Cs k gt C k ) 2 (14) k=1 (ii) (11) seed C s G Cs C G C D(G Cs, G C ) (15) D(G C(s), G C )= (G Cs G C ) T Σ 1 (G Cs G C ) (15)
9 Web 2 31
10 Vol. 16 No. 5 October 2009 Σ seed C s C s C s = {w p1 C s, w p2 C s,, w pm C s } G Cs G Cs = 1 m m i=1 w pi C s Σ ij (16) Σ ij = 1 m m i=1 (w pi C s G Cs )(w pj C s G Cs ) T (16) Σ Σ 11 Σ 12 Σ 1m Σ 21 Σ 22 Σ 2m Σ = Σ m1 Σ m2 Σ mm (iii) (ii) seed C sj Web C sj G C C l G C l (Diday and Govaert 1977) (1) C sj Web w pi v (w pi v) d sj (w pi, v) (17) d sj (w pi, v) =(w pi v) T M 1 (w pi v) (17) M sj C sj C sj C sj = {w p1 C sj, w p2 C sj,, w pm C sj } G C 32
11 Web G C = 1 m m i=1 w pi C sj M ij (18) M ij = 1 m m i=1 (w pi C sj G Cs p j )(w j C sj G C ) T (18) M sj M 11 M 12 M 1m M 21 M 22 M 2m M sj = M m1 M m2 M mm (2) Δ sj (v, M sj )= w p i C sj d sj (w pi, v) = (w pi v) T M 1 (w pi v) w p i C sj C sj L sj S sj (i) C sj M sj Δ sj L sj L sj =argmin v w p i C sj (w pi v) T M 1 (w pi v) (19) (19) C sj G G v G L sj = v G (ii) (i) L sj = v G det(m sj )=1 Δ sj S sj S sj =argmin M sj w p i C sj (w pi v G ) T M 1 (w pi v G ) (20) S sj C sj M sj (21) (Diday and Govaert 1977) 33
12 Vol. 16 No. 5 October 2009 S sj =(det(m sj )) 1/m M 1 (21) det(m sj ) 0 m seed C sj Web S sj S sj C sj G C Cl G C l (22) D(G C, G C l )= (G C G C l ) T S 1 (G C G C l ) (22) (22) (1) (2) C sj Web (21) S sj (15) Web People Search Task (Artiles, Gonzalo, and Sekine 2007) WePS WePS Yahoo! 5 API 100 7,900 Web 1 Web 6 Porter Stemmer (Porter 1980) 7 WePS WePS 3.2 purity inverse purity F (Hotho, Nürnberger, and Paaß 2005) Web People Search Task purity C ftp://ftp.cs.cornell.edu/pub/smart/english.stop 7 martin/porterstemmer/ 34
13 Web 1 WePS (A) (A) 1 Wikipedia ECDL Wikipedia ACL *ECDL: European Conference on Digital Libraries, ACL: Association for Computational Linguistics L n purity (23) Purity = i C i n max P recision(c i,l j ) (23) L j C i P recision(c i,l j ) (24) P recision(c i,l j )= C i Lj (24) C i inverse purity inverse purity (25) InversePurity = j L j n max Recall(C i,l j ) (25) L j C i Recall(C i,l j ) (26) Recall(C i,l j )= C i Lj (26) L j purity inverse purity F (27) 1 F = α 1 Purity +(1 α) 1 InverseP urity (27) 35
14 Vol. 16 No. 5 October 2009 α = α = F F 0.5 F seed (a) Wikipedia (Remy 2002) (b) Web Web c seed C sj C i 2 (11) C i Web w p l C i (l =1,,n i ) (12) (11) c D(G Ci, G C ) 0 w p WePS 2 seed (a) (b) 7 seed 0.1 c 50 seed 7 seed 2 4 c F 0.5 F seed 2 4 c WePS 2 c Seed page c F 0.5 F 0.2 Seed page c F 0.5 F Wikipedia article Web page Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages
15 Web 3 c Seed page c F 0.5 F 0.2 Seed page c F 0.5 F Wikipedia article Web page Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages c Seed page c F 0.5 F 0.2 Seed page c F 0.5 F Wikipedia article Web page Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Wikipedia articles Web pages Purity Inverse purity F 0.5 F (1) 5 (2) seed seed seed (a) Wikipedia (b) 1 Web 3.1 WePS Wikipedia Wikipedia seed Web 1 Web WePS Wikipedia 14 1 Web seed 37
16 Vol. 16 No. 5 October 2009 Wikipedia Bunescu (Bunescu and Pasca 2006) Wikipedia 6 seed seed F (F 0.5 =0.68 F 0.2 =0.66) seed seed seed 7 2 seed cannot-link Web 3 4, Wikipedia 7 Web (F ) 2 seed 1 Klein (Klein et al. 2002) Xing (Xing et al. 2003) Bar-Hillel (Bar-Hillel et al. 2003) seed Web seed Web seed seed Distance measure Seed page* Purity Inverse purity F 0.5 F 0.2 (i) Euclidean distance (a) (b) (ii) Mahalanobis distance (a) (b) (iii) Adapative Mahalanobis distance (a) (b) *(a) and (b) in Seed page denote Wikipedia article and top-ranked Web page, respectively. 38
17 Web 3 seed (7 Wikipedia ) 4 seed ( 7 Web ) Wikipedia (F 0.5 =0.76,F 0.2 =0.74) (i) seed Web (ii) (i) WePS F seed Web 5 (ii) 39
18 Vol. 16 No. 5 October seed (Wikipedia ), seed Web ( w s, ) seed (Wikipedia ) seed ( w s, ) WePS F seed 6 (i) (ii) Web People Search Task 3 (F )
19 Web 7 Web People Search Task 3 Team-ID Purity Inverse purity F 0.5 F 0.2 CU COMSEM (Chen and Martin 2007) IRST-BP (Popescu and Magnini 2007) PSNUS (Elmacioglu, Tan, Yan, Kan, and Lee 2007) Our proposed method (with adaptive Mahalanobis distance) Using full text (Sec ) 1 Wikipedia article Web page Wikipedia articles Web pages Using fragments (Sec ) (i) 2 and 3 sentences in 5 Wikipedia seed pages and a search result Web page, respectively (ii) Snippet and 3 sentences in 5 Wikipedia seed pages (11) seed Web 7 Wikipedia 7 Web seed PC (CPU: Intel Pentium M 2.0 GHz Memory: 2 GByte OS: Windows XP) Perl (11) c 2 4 c = c 50 (11) c Web 41
20 Vol. 16 No. 5 October seed 5 purity (0.67) inverse purity (0.48) purity F F 0.5 =0.52 F 0.2 = purity ( ) 5 purity (0.67) inverse purity ( ) (0.48) inverse purity F seed seed 6 seed Wikipedia F (F 0.5 =0.68 F 0.2 =0.66) seed 3 4 seed (F ) seed 7 seed 5 Web Web purity 42
21 Web Bar-Hillel (Bar-Hillel et al. 2003) Xing (Xing et al. 2003) Klein (Klein et al. 2002) 1 Klein 2 (x i,x j ) 0 2 (max i,j D ij )+1 Xing Bar-Hillel seed seed seed Wikipedia Web Web seed Wikipedia WePS (i) seed Web 5 Web 5 seed Web 2 3 F (F 0.5 =0.79 F 0.2 =0.80) WePS [purity:0.80 inverse purity:0.83, F 0.5 =0.81 F 0.2 =0.82] F α =0.5 7 Web People Search Task (Artiles et al. 2007) 1 (CU COMSEM) (2) Wikipedia 16 Wikipedia 10 ( 1 (A) ) ACL 06 Wikipedia 6 1 (B) Wikipedia seed 8 (A) (B) seed Wikipedia (B) Web Wikipedia 43
22 Vol. 16 No. 5 October 2009 seed seed Web 7 inverse purity Web People Search Task 3 seed 5 2 gain Web People Search Task seed Wikipedia 5 CU COMSEM 7 F (F 0.5 =0.78 F 0.2 =0.83) F (F 0.5 =0.81 F 0.2 =0.84) F F URL IRST-BP 7 F (F 0.5 =0.75 F 0.2 =0.77) (F 0.5 =0.76 F 0.2 =0.81) F F Wikipedia seed (A) Wikipedia 10 (B) Wikipedia ACL 06 6 F 0.5 F 0.2 F 0.5 F 0.2 (A) (B) Web People Search Task 3 Team-ID Purity Inverse purity F 0.5 F 0.2 CU COMSEM IRST-BP PSNUS
23 Web PSNUS NE TF-IDF 7 F (F 0.5 =0.75 F 0.2 =0.78) F F 0.5 =0.78 F 0.2 =0.82 F F gain 7 F 0.5 =0.81 F 0.2 =0.82 F CU COMSEM F gain Web 5 F (F 0.5 =0.52 F 0.2 =0.49) F F WePS (ii) seed 6 seed 6 seed 3 F (F 0.5 =0.64 F 0.2 =0.67) WePS [purity:0.70 inverse purity:0.62 F 0.5 =0.66 F 0.2 =0.68] Web People Search Task 3 seed 3 Web seed Wikipedia seed 2 Web 3 (22) Web 5 seed seed Web
24 Vol. 16 No. 5 October Web seed [purity:0.80 inverse purity:0.83 F 0.5 :0.81 F 0.2 :0.82] seed cannot-link seed seed Web seed Web Web seed seed Artiles, J., Gonzalo, J., and Sekine, S. (2007). The SemEval-2007 WePS Evaluation: Establishing a Benchmark for the Web People Search Task. In Proc. of the Semeval 2007, Association for Computational Linguistics (ACL), pp Bar-Hillel, A., Hertz, T., and Shental, N. (2003). Learning Distance Functions Using Equivalence Relations. In Proc. of the 20th International Conference on Machine Learning (ICML 2003), pp Basu, S., Banerjee, A., and Mooney, R. (2002). Semi-supervised Clustering by Seeding. In Proc. of the 19th International Conference on Machine Learning (ICML 2002), pp Bekkerman, R., El-Yaniv, R., and McCallum, A. (2005). Multi-way Distributional Clustering via Pairwise Interactions. In Proc. of the 22nd International Conference on Machine Learning (ICML2005), pp Bollegala, D., Matsuo, Y., and Ishizuka, M. (2006). Extracting Key Phrases to Disambiguate Personal Names on the Web. In Proc. of the 7th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2006), pp
25 Web Bookstein, A. and Swanson, D. R. (1974). Probabilistic Models for Automatic Indexing. Journal of the American Society for Information Science, 25 (5), pp Bunescu, R. and Pasca, M. (2006). Using Encyclopedic Knowledge for Named Entity Disambiguation. In Proc. of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), pp Chen, Y. and Martin, J. (2007). CU-COMSEM: Exploring Rich Features for Unsupervised Web Personal Name Disambiguation. In Proc. of the Semeval 2007, Association for Computational Linguistics (ACL), pp Church, K. W. and Gale, W. A. (1995a). Inverse Document Frequency (IDF): A Measure of Deviation from Poisson. In Proc. of the 3rd Workshop on Very Large Corpora, pp Church, K. W. and Gale, W. A. (1995b). Poisson Mixtures. Journal of Natural Language Engineering, 1 (2), pp Diday, E. and Govaert, G. (1977). Classification Automatique Avec Distances Adaptatives. R.A.I.R.O. Informatique Computer Science, 11 (4), pp Elmacioglu, E., Tan, Y. F., Yan, S., Kan, M.-Y., and Lee, D. (2007). PSNUS: Web People Name Disambiguation by Simple Clustering with Rich Features. In Proc.oftheSemeval 2007, Association for Computational Linguistics (ACL), pp Hotho, A., Nürnberger, A., and Paaß, G. (2005). A Brief Survey of Text Mining. GLDV-Journal for Computational Linguistics and Language Technology, 20 (1), pp Jones, K. S. (1973). Index Term Weighting. Information Strage and Retrieval, 9 (11), pp Klein, D., Kamvar, S. D., and Manning, C. D. (2002). From Instance-level Constraints to Spacelevel Constraints: Making the Most of Prior Knowledge in Data Clustering. In Proc. of the 19th International Conference on Machine Learning (ICML 2002), pp MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. In Proc. of the 5th Berkeley Symposium on Mathmatical Statistics and Probability, pp Mann, G. S. and Yarowsky, D. (2003). Unsupervised Personal Name Disambiguation. In Proc. of the 7th Conference on Natural Language Learning (CoNLL-2003), pp Papineni, K. (2001). Why Inverse Document Frequency? In Proc. of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2001), pp Pedersen, T., Purandare, A., and Kulkarni, A. (2005). Name Discrimination by Clustering Similar Contexts. In Proc. of the 6th International Conference on Computational Linguistics 47
26 Vol. 16 No. 5 October 2009 and Intelligent Text Processing (CICLing 2005), pp Popescu, O. and Magnini, B. (2007). IRST-BP: Web People Search Using Name Entities. In Proc. of the Semeval 2007, Association for Computational Linguistics (ACL), pp Porter, M. F. (1980). An Algorithm for Suffix Stripping. Program, 14 (3), pp Remy, M. (2002). Wikipedia: The Free Encyclopedia. Online Information Review, 26 (6), p Rocchio, J. (1971). Relevance Feedback in Information Retrieval. In Salton, G. (Ed.), The Smart Retrieval System: Experiments in Automatic Document Processing, pp Prentice-Hall, Englewood Cliffs, NJ. Salton, G. and McGill, M. J. (1983). Introduction to Modern Information Retrieval. McGraw-Hill. Shental, N., Hertz, T., Weinshall, D., and Pavel, M. (2002). Adjustment Learning and Relevant Component Analysis. In Proc. of the 7th European Conference on Computer Vision (ECCV 2002), pp Wagstaff, K. and Cardie, C. (2000). Clustering with Instance-level Constraints. In Proc.ofthe 17th International Conference on Machine Learning (ICML 2000), pp Wagstaff, K., Rogers, S., and Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proc. of the 18th International Conference on Machine Learning (ICML 2001), pp Xing, E. P., Ng, A. Y., Jordan, M. I., and Russell, S. J. (2003). Distance Metric Learning with Application to Clustering with Side-Information. Advances in Neural Information Processing Systems, 15, pp KDDI IEEE ACM AAAI
27 Web AAAI ACL
IPSJ-TOD
Vol. 3 No. 2 91 101 (June 2010) 1 1 1 2 1 TSC2 Automatic Evaluation of Text Summaries by Using Paraphrase Kazuho Hirahara, 1 Hidetsugu Nanba, 1 Toshiyuki Takezawa 1 and Manabu Okumura 2 The evaluation
More information1 AND TFIDF Web DFIWF Wikipedia Web Web 2. 3. 4. AND 5. Wikipedia AND 6. Wikipedia Web 7. 8. 2. Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [
DEIM Forum 2015 B1-5 606 8501 606 8501 E-mail: komurasaki@dl.kuis.kyoto-u.ac.jp, tajima@i.kyoto-u.ac.jp Web Web AND AND Web 1. Twitter Facebook SNS Web Web Web Web [5] Bollegala [2] Web Web 1 Google Microsoft
More informationkut-paper-template.dvi
14 Application of Automatic Text Summarization for Question Answering System 1030260 2003 2 12 Prassie Posum Prassie Prassie i Abstract Application of Automatic Text Summarization for Question Answering
More information1 1 tf-idf tf-idf i
14 A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles 1055104 2003 1 31 1 1 tf-idf tf-idf i Abstract A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles
More informationIT i
27 The automatic extract of know-how search tag using a thesaurus 1160374 2016 2 26 IT i Abstract The automatic extract of know-how search tag using a thesaurus In recent years, a number of organizational
More informationMining Social Network of Conference Participants from the Web
Social Network Mining Social network Semantic Web, KM, Our lives are enormously influenced by relations to others. SNS Mixi, myspace, LiveJournal, Yahoo!360 FOAF WebBlog Web mining Social network mining
More informationuntitled
580 26 5 SP-G 2011 AI An Automatic Question Generation Method for a Local Councilor Search System Yasutomo KIMURA Hideyuki SHIBUKI Keiichi TAKAMARU Hokuto Ototake Tetsuro KOBAYASHI Tatsunori MORI Otaru
More informationTrial for Value Quantification from Exceptional Utterances 37-066593 1 5 1.1.................................. 5 1.2................................ 8 2 9 2.1.............................. 9 2.1.1.........................
More informationTF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat
1 1 2 1. TF-IDF TDF-IDF TDF-IDF. 3 18 6 Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Satoshi Date, 1 Teruaki Kitasuka, 1 Tsuyoshi Itokawa 2
More informationNo. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1
ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?
More information1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan
SNS 1,a) 2 3 3 2012 3 30, 2012 10 10 SNS SNS Development of Firefighting Knowledge Succession Support SNS in Tokyo Fire Department Koutarou Ohno 1,a) Yuki Ogawa 2 Hirohiko Suwa 3 Toshizumi Ohta 3 Received:
More informationi
2011 2012 3 26 ( : A8TB2114) i 1 1 2 3 2.1 Espresso................................. 3 2.2 CPL................................... 4 2.3.................................... 5 2.4.........................
More informationA Japanese Word Dependency Corpus ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹
A Japanese Word Dependency Corpus 2015 3 18 Special thanks to NTT CS, 1 /27 Bunsetsu? What is it? ( ) Cf. CoNLL Multilingual Dependency Parsing [Buchholz+ 2006] (, Penn Treebank [Marcus 93]) 2 /27 1. 2.
More information<> <name> </name> <body> <></> <> <title> </title> <item> </item> <item> 11 </item> </>... </body> </> 1 XML Web XML HTML 1 name item 2 item item HTML
DEWS2008 C6-4 XML 606-8501 E-mail: yyonei@db.soc.i.kyoto-u.ac.jp, {iwaihara,yoshikawa}@i.kyoto-u.ac.jp XML XML XML, Abstract Person Retrieval on XML Documents by Coreference that Uses Structural Features
More informationComputational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego
Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure
More informationIPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St
1 2 1, 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical Structures based on Phrase Similarity Yuma Ito, 1 Yoshinari Takegawa, 2 Tsutomu Terada 1, 3 and Masahiko Tsukamoto
More informationmain.dvi
305 8550 1 2 CREST fujii@slis.tsukuba.ac.jp 1 7% 2 2 3 PRIME Multi-lingual Information Retrieval 2 2.1 Cross-Language Information Retrieval CLIR 1990 CD-ROM a. b. c. d. b CLIR b 70% CLIR CLIR 2.2 (b) 2
More information17 Proposal of an Algorithm of Image Extraction and Research on Improvement of a Man-machine Interface of Food Intake Measuring System
1. (1) ( MMI ) 2. 3. MMI Personal Computer(PC) MMI PC 1 1 2 (%) (%) 100.0 95.2 100.0 80.1 2 % 31.3% 2 PC (3 ) (2) MMI 2 ( ),,,, 49,,p531-532,2005 ( ),,,,,2005,p66-p67,2005 17 Proposal of an Algorithm of
More informationgengo.dvi
4 97.52% tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation
More information& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro
TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
More informationWeb Web Web Web Web, i
22 Web Research of a Web search support system based on individual sensitivity 1135117 2011 2 14 Web Web Web Web Web, i Abstract Research of a Web search support system based on individual sensitivity
More informationuntitled
K-Means 1 5 2 K-Means 7 2.1 K-Means.............................. 7 2.2 K-Means.......................... 8 2.3................... 9 3 K-Means 11 3.1.................................. 11 3.2..................................
More information1 3 1.1................................. 3 1.2................................... 4 1.2.1................... 4 1.2.2..................... 4 1.2.3.....
2012 STUDIES ON RANKING DOCUMENTS WITH QUERY-INTENT SENSITIVITY 11R3129 Shota HATAKENAKA 1 3 1.1................................. 3 1.2................................... 4 1.2.1................... 4 1.2.2.....................
More informationDPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
More information独立行政法人情報通信研究機構 Development of the Information Analysis System WISDOM KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the infor
独立行政法人情報通信研究機構 KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the information analysis system WISDOM as a research result of the second medium-term plan. WISDOM has functions that
More informationクラスタリング クラスタリングとは クラスタの良さを類似度 目的関数で定義 困難 教師ありクラスタリング 類似度 目的関数ではなく 教師情報 制約を導入 教師情報 制約に一致するクラスタが良い クラスタリング問題を 絶対クラスタリングと相対クラスタリング に分けて考える必要 2
教師ありクラスタリング と 絶対/相対クラスタリング 神嶌 敏弘 http://www.kamishima.net/ 産業技術総合研究所 2006年情報論的学習理論ワークショップ(IBIS2006) 2006/10/31-11/2 1 クラスタリング クラスタリングとは クラスタの良さを類似度 目的関数で定義 困難 教師ありクラスタリング 類似度 目的関数ではなく 教師情報 制約を導入 教師情報 制約に一致するクラスタが良い
More informationIPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing
DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing Youhei Namiki 1 and Yutaka Akiyama 1 Pyrosequencing, one of the DNA sequencing technologies, allows us to determine
More information[7] [10] Web Web RDF Resource Description Framework subjectpredicate object Web Web Web Web Web 2 Web 3 4 5 6 2. Web MUC(Message Understanding Confere
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. Web 113 8656 7 3 1 113 8656 7 3 1 E-mail: {tjstkm,jmori,ishizuka}@mi.ci.i.u-tokyo.ac.jp Web Web Web Web
More informationIT,, i
22 Retrieval support system using bookmarks that are shared in an organization 1110250 2011 3 17 IT,, i Abstract Retrieval support system using bookmarks that are shared in an organization Yoshihiko Komaki
More information% 95% 2002, 2004, Dunkel 1986, p.100 1
Blended Learning 要 旨 / Moodle Blended Learning Moodle キーワード:Blended Learning Moodle 1 2008 Moodle e Blended Learning 2009.. 1994 2005 1 2 93% 95% 2002, 2004, 2011 2011 1 Dunkel 1986, p.100 1 Blended Learning
More information( )
NAIST-IS-MT1051071 2012 3 16 ( ) Pustejovsky 2 2,,,,,,, NAIST-IS- MT1051071, 2012 3 16. i Automatic Acquisition of Qualia Structure of Generative Lexicon in Japanese Using Learning to Rank Takahiro Tsuneyoshi
More information2007/2 Vol. J90 D No Web 2. 1 [3] [2], [11] [18] [14] YELLOW [16] [8] tfidf [19] 2. 2 / 30% 90% [24] 2. 3 [4], [21] 428
Informative Summarization Method by Key Sentences Extraction Considering Sub-Topics Naoki SAGARA, Wataru SUNAYAMA, and Masahiko YACHIDA 1. 1990 WWW World Wide Web Web [15] Graduate School of Engineering
More informationIPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came
3DCG 1,a) 2 2 2 2 3 On rigid body animation taking into account the 3D computer graphics camera viewpoint Abstract: In using computer graphics for making games or motion pictures, physics simulation is
More informationVol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m
Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki
More information2. Twitter Twitter 2.1 Twitter Twitter( ) Twitter Twitter ( 1 ) RT ReTweet RT ReTweet RT ( 2 ) URL Twitter Twitter 140 URL URL URL 140 URL URL
1. Twitter 1 2 3 3 3 Twitter Twitter ( ) Twitter (trendspotter) Twitter 5277 24 trendspotter TRENDSPOTTER DETECTION SYSTEM FOR TWITTER Wataru Shirakihara, 1 Tetsuya Oishi, 2 Ryuzo Hasegawa, 3 Hiroshi Hujita
More informationkut-paper-template.dvi
26 Discrimination of abnormal breath sound by using the features of breath sound 1150313 ,,,,,,,,,,,,, i Abstract Discrimination of abnormal breath sound by using the features of breath sound SATO Ryo
More information. Yahoo! 1!goo 2 QA..... QA Web Web 2 3 4 5 6 7 8 2. [1]Web Web Yin [2] Web Web Web. [3] Web Wikipedia 1 2
DEIM Forum 211 F6-3 Web 35 855 1 2 35 855 1 2 11 843 2 1 2 E-mail: s913153@klis.tsukuba.ac.jp, {yohei,satoh}@slis.tsukuba.ac.jp, kando@nii.ac.jp QA Web Web Web QA Diversified-query Generating System Using
More information: ( 1) () 1. ( 1) 2. ( 1) 3. ( 2)
Acquiring Organized Information from News by Incremental Theme Refinements 1 1 1 Yutaro Taniguchi 1 Tetsunori Kobayashi 1 Yoshihiko Hayashi 1 1 1 School of Science and Engineering, Waseda University Abstract:
More information24 Region-Based Image Retrieval using Fuzzy Clustering
24 Region-Based Image Retrieval using Fuzzy Clustering 1130323 2013 3 9 Visual-key Image Retrieval(VKIR) k-means Fuzzy C-means 2 200 2 2 20 VKIR 5 18% 54% 7 30 Fuzzy C-means i Abstract Region-Based Image
More information[1], B0TB2053, 20014 3 31. i
B0TB2053 20014 3 31 [1], B0TB2053, 20014 3 31. i 1 1 2 3 2.1........................ 3 2.2........................... 3 2.3............................. 4 2.3.1..................... 4 2.3.2....................
More information3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
(MIRU2012) 2012 8 820-8502 680-4 E-mail: {d kouno,shimada,endo}@pluto.ai.kyutech.ac.jp (1) (2) (3) (4) 4 AdaBoost 1. Kanade [6] CLAFIC [12] EigenFace [10] 1 1 2 1 [7] 3 2 2 (1) (2) (3) (4) 4 4 AdaBoost
More information情報処理学会研究報告 IPSJ SIG Technical Report Vol.2013-CVIM-186 No /3/15 EMD 1,a) SIFT. SIFT Bag-of-keypoints. SIFT SIFT.. Earth Mover s Distance
EMD 1,a) 1 1 1 SIFT. SIFT Bag-of-keypoints. SIFT SIFT.. Earth Mover s Distance (EMD), Bag-of-keypoints,. Bag-of-keypoints, SIFT, EMD, A method of similar image retrieval system using EMD and SIFT Hoshiga
More information(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
More information1 Web,.,, Web..,, Web.,,,.,,,., CGI.,, Web, Web.,,. PC,,.
Web 1 Web,.,, Web..,, Web.,,,.,,,., CGI.,, Web, Web.,,. PC,,. 2 1 6 1.1............................................... 6 1.2.............................................. 6 1.3...............................................
More information2) TA Hercules CAA 5 [6], [7] CAA BOSS [8] 2. C II C. ( 1 ) C. ( 2 ). ( 3 ) 100. ( 4 ) () HTML NFS Hercules ( )
1,a) 2 4 WC C WC C Grading Student programs for visualizing progress in classroom Naito Hiroshi 1,a) Saito Takashi 2 Abstract: To grade student programs in Computer-Aided Assessment system, we propose
More information4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
More information(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
More information1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
More informationIPSJ SIG Technical Report iphone iphone,,., OpenGl ES 2.0 GLSL(OpenGL Shading Language), iphone GPGPU(General-Purpose Computing on Graphics Proc
iphone 1 1 1 iphone,,., OpenGl ES 2.0 GLSL(OpenGL Shading Language), iphone GPGPU(General-Purpose Computing on Graphics Processing Unit)., AR Realtime Natural Feature Tracking Library for iphone Makoto
More informationInput image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
More informationActionScript Flash Player 8 ActionScript3.0 ActionScript Flash Video ActionScript.swf swf FlashPlayer AVM(Actionscript Virtual Machine) Windows
ActionScript3.0 1 1 YouTube Flash ActionScript3.0 Face detection and hiding using ActionScript3.0 for streaming video on the Internet Ryouta Tanaka 1 and Masanao Koeda 1 Recently, video streaming and video
More informationIPSJ SIG Technical Report Vol.2010-SLDM-144 No.50 Vol.2010-EMB-16 No.50 Vol.2010-MBL-53 No.50 Vol.2010-UBI-25 No /3/27 Twitter IME Twitte
Twitter 1 1 1 IME Twitter 2009 12 15 2010 2 1 13590 4.83% 8.16% 2 3 Web 10 45% Relational Analysis between User Context and Input Word on Twitter Yutaka Arakawa, 1 Shigeaki Tagashira 1 and Akira Fukuda
More information21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G
ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
More information‰gficŒõ/’ÓŠ¹
The relationship between creativity of Haiku and idea search space YOSHIDA Yasushi This research examined the relationship between experts' ranking of creative Haiku (a Japanese character poem including
More information258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
More informationfiš„v5.dvi
(2001) 49 2 293 303 VRML 1 2 3 2001 4 12 2001 10 16 Web Java VRML (Virtual Reality Modeling Language) VRML Web VRML VRML VRML VRML Web VRML VRML, 3D 1. WWW (World Wide Web) WWW Mittag (2000) Web CGI Java
More informationIPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
More informationDEIM Forum 2010 A Web Abstract Classification Method for Revie
DEIM Forum 2010 A2-2 305 8550 1 2 305 8550 1 2 E-mail: s0813158@u.tsukuba.ac.jp, satoh@slis.tsukuba.ac.jp Web Abstract Classification Method for Reviews using Degree of Mentioning each Viewpoint Tomoya
More informationjohnny-paper2nd.dvi
13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which
More information,,,,., C Java,,.,,.,., ,,.,, i
24 Development of the programming s learning tool for children be derived from maze 1130353 2013 3 1 ,,,,., C Java,,.,,.,., 1 6 1 2.,,.,, i Abstract Development of the programming s learning tool for children
More information21 A contents organization method for information sharing systems
21 A contents organization method for information sharing systems 1125140 2010 3 4 IT i Abstract A contents organization method for information sharing systems Aoki, Wataru Organizations to share information,
More information2015 9
JAIST Reposi https://dspace.j Title ウェブページからのサイト情報 作成者情報の抽出 Author(s) 堀, 達也 Citation Issue Date 2015-09 Type Thesis or Dissertation Text version author URL http://hdl.handle.net/10119/12932 Rights Description
More information2016 10 31 1. 1.1 20 1 1993 20 2 2 1 industrial society 2 2 169 2014 3 1.2 4 5 6 3 1.3 4 5 1973 6 170 7 8 9 7 ISO/IEC 9126 11 8 1 9 ABS ABS ABS ABS 171 2. 2.1 1960 10 11 12 13 10 1964 IBM S/360 11 16 FORTRAN
More information1_26.dvi
C3PV 1,a) 2,b) 2,c) 3,d) 1,e) 2012 4 20, 2012 10 10 C3PV C3PV C3PV 1 Java C3PV 45 38 84% Programming Process Visualization for Supporting Students in Programming Exercise Hiroshi Igaki 1,a) Shun Saito
More informationVol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132
2000 6 5 6 :, Supporting Conference Program Production Using Natural Language Processing Technologies Hiromi itoh Ozaku Masao Utiyama Masaki Murata Kiyotaka Uchimoto and Hitoshi Isahara We applied natural
More information1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan
1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the
More information( : A8TB2163)
2011 2012 3 26 ( : A8TB2163) ( A B [1] A B A B B i 1 1 2 3 2.1... 3 2.1.1... 3 2.1.2... 4 2.2... 5 3 7 3.1... 7 3.2... 7 3.3 A B... 7 4 8 4.1... 8 4.1.1... 9 4.1.2... 9 4.1.3... 9 4.1.4... 10 4.2 A B...
More informationskeiji.final.dvi
HTML HTML 1) HTML HTML 2) df idf 3) 4) : World Wide Web Automatic acquisition of hyponymy relations from HTML documents This paper describes an automatic acquisition method for hyponymy relations. Hyponymy
More informationSOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i
20 SOM Development of Syllabus Vsualization System using Spherical Self-Organizing Maps 1090366 2009 3 5 SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i Abstract Development of Syllabus Vsualization
More information_314I01BM浅谷2.indd
587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4 588 31 4 2016
More information1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
More informationWeb サイト作成者によって設定された Web リンク システムが作成した Web リンク データ 静的リンク 学部ページ 大学 ページ 就職関連ページ 入試関連ページ 利用者の要求 データベース 動的リンク データ工学研究室ページ ベース研究室ページ 大学ページ 3. Web 学科ページ データ工
DEWS2005 4B-o2 Web 525-8577 1-1-1 525-8577 1-1-1 E-mail: {nakatani,suzuki,kawagoe}@coms.ics.ritsumei.ac.jp Web Web Web Web 1) Web 2) Web 1) Web 2) Web Web Web Web Web Web Web Web Abstract Automatic and
More information1 Broder Navigational URL URL Informational Web Transactional Web Web Web 2 Broder [16] SearchLife Broder [16] Daniel [17] Broder
DEIM Forum 2010 B9-4 432 8011 3 5 1 432 8011 3 5 1 E-mail: gs08062@s.inf.shizuoka.ac.jp, {yokoyama,fukuta,ishikawa}@inf.shizuoka.ac.jp Web Web. Evaluation of a Multiple Viewpoints Clustering Search Engine
More informationIPSJ SIG Technical Report Pitman-Yor 1 1 Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Aki
Pitman-Yor Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Akira Shirai and Tadahiro Taniguchi Although a lot of melody generation method has been
More informationIPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie
1,a) 1 1,2 Sakriani Sakti 1 1 1 1. [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Science and Technology 2 Japan Science and Technology Agency a) ishikawa.yoko.io5@is.naist.jp 2. 1 Belief-Desire theory
More informationIPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe
1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
More informationVol. 48 No. 3 Mar PM PM PMBOK PM PM PM PM PM A Proposal and Its Demonstration of Developing System for Project Managers through University-Indus
Vol. 48 No. 3 Mar. 2007 PM PM PMBOK PM PM PM PM PM A Proposal and Its Demonstration of Developing System for Project Managers through University-Industry Collaboration Yoshiaki Matsuzawa and Hajime Ohiwa
More information22 Google Trends Estimation of Stock Dealing Timing using Google Trends
22 Google Trends Estimation of Stock Dealing Timing using Google Trends 1135064 3 1 Google Trends Google Trends Google Google Google Trends Google Trends 2006 Google Google Trend i Abstract Estimation
More informationSERPWatcher SERPWatcher SERP Watcher SERP Watcher,
SERPWatcher 112-8610 2-1-1 112-8610 2-1-1 229-8558 5-10-1 E-mail: nakabe@db.is.ocha.ac.jp, chiemi@is.ocha.ac.jp SERPWatcher SERP Watcher SERP Watcher, SERP Analysis of transition of ranking in SERP Watcher
More informationWII-D 2017 (1) (2) (1) (2) [Tanaka 07] [ 04] [ 10] [ 13, 13], [ 08] [ 13] (1) (2) 2 2 e.g., Wikipedia [ 14] Wikipedia [ 14] Linked Open
Web 2017 Original Paper Supporting Exploratory Information Access Based on Comic Content Information 1 Ryo Yamashita Byeongseon Park Mitsunori Matsushita Nomura Research Institute, LTD. r-yamashita@nri.co.jp
More informationIPSJ SIG Technical Report Vol.2010-GN-74 No /1/ , 3 Disaster Training Supporting System Based on Electronic Triage HIROAKI KOJIMA, 1 KU
1 2 2 1, 3 Disaster Training Supporting System Based on Electronic Triage HIROAKI KOJIMA, 1 KUNIAKI SUSEKI, 2 KENTARO NAGAHASHI 2 and KEN-ICHI OKADA 1, 3 When there are a lot of injured people at a large-scale
More information2
NTT 2012 NTT Corporation. All rights reserved. 2 3 4 5 Noisy Channel f : (source), e : (target) ê = argmax e p(e f) = argmax e p(f e)p(e) 6 p( f e) (Brown+ 1990) f1 f2 f3 f4 f5 f6 f7 He is a high school
More information自然言語処理24_705
nwjc2vec: word2vec nwjc2vec nwjc2vec nwjc2vec 2 nwjc2vec 7 nwjc2vec word2vec nwjc2vec: Word Embedding Data Constructed from NINJAL Web Japanese Corpus Hiroyuki Shinnou, Masayuki Asahara, Kanako Komiya
More information1 Fig. 2 2 Fig. 1 Sample of tab UI 1 Fig. 1 that changes by clicking tab 5 2. Web HTML Adobe Flash Web ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) 3 Web 2.1 Web Goo
Web 1,a) 1,b) Web Web HTML Indicating Important Parts in Searched Web Pages by Retrieval Terms Yokoo Shunichi 1,a) Yoshiura Noriaki 1,b) Abstract: Users cannot always find retrieval terms immediately in
More informationBOK body of knowledge, BOK BOK BOK 1 CC2001 computing curricula 2001 [1] BOK IT BOK 2008 ITBOK [2] social infomatics SI BOK BOK BOK WikiBOK BO
DEIM Forum 2012 C8-5 WikiBOK 252 5258 5 10 1 E-mail: shunsuke.shibuya@gmail.com, {kaz,masunaga}@si.aoyama.ac.jp, {yabuki,sakuta}@it.aoyama.ac.jp Body Of Knowledge, BOK BOK BOK BOK BOK, BOK Abstract Extention
More information3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3
Vol. 52 No. 12 3806 3816 (Dec. 2011) 1 1 Discovering Latent Solutions from Expressions of Dissatisfaction in Blogs Toshiyuki Sakai 1 and Ko Fujimura 1 This paper aims to find the techniques or goods that
More informationmain.dvi
DEIM Forum 2018 J7-3 305-8573 1-1-1 305-8573 1-1-1 305-8573 1-1-1 () 151-0053 1-3-15 6F URL SVM Identifying Know-How Sites basedonatopicmodelandclassifierlearning Jiaqi LI,ChenZHAO, Youchao LIN, Ding YI,ShutoKAWABATA,
More information2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2
Curved Document Imaging with Eye Scanner Toshiyuki AMANO, Tsutomu ABE, Osamu NISHIKAWA, Tetsuo IYODA, and Yukio SATO 1. Shape From Shading SFS [1] [2] 3 2 Department of Electrical and Computer Engineering,
More informationIPSJ SIG Technical Report Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for
1 2 3 3 1 Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for Mobile Terminals Kaoru Wasai 1 Fumio Sugai 2 Yosihiro Kita 3 Mi RangPark 3 Naonobu
More informationIPSJ SIG Technical Report Vol.2009-HCI-134 No /7/17 1. RDB Wiki Wiki RDB SQL Wiki Wiki RDB Wiki RDB Wiki A Wiki System Enhanced by Visibl
1. RDB Wiki 1 1 2 Wiki RDB SQL Wiki Wiki RDB Wiki RDB Wiki A Wiki System Enhanced by Visible RDB Operations Toshiya Okumura, 1 Minoru Terada 1 and Kazutaka Maruyama 2 Although Wiki systems can easily be
More informationVol. 42 No MUC-6 6) 90% 2) MUC-6 MET-1 7),8) 7 90% 1 MUC IREX-NE 9) 10),11) 1) MUCMET 12) IREX-NE 13) ARPA 1987 MUC 1992 TREC IREX-N
Vol. 42 No. 6 June 2001 IREX-NE F 83.86 A Japanese Named Entity Extraction System Based on Building a Large-scale and High-quality Dictionary and Pattern-matching Rules Yoshikazu Takemoto, Toshikazu Fukushima
More informationDEIM Forum 2009 E
DEIM Forum 2009 E5-3 464-8601 1 606-8501 464 8601 1 E-mail: lifushi@arch.itc.nagoya-u.ac.jp, mayumi@mm.media.kyoto-u.ac.jp, {hirano,kajita,mase}@itc.nagoya-u.ac.jp Abstract Study on a Recipe Recommendation
More information[4], [5] [6] [7] [7], [8] [9] 70 [3] 85 40% [10] Snowdon 50 [5] Kemper [3] 2.2 [11], [12], [13] [14] [15] [16]
1,a) 1 2 1 12 1 2Type Token 2 1 2 1. 2013 25.1% *1 2012 8 2010 II *2 *3 280 2025 323 65 9.3% *4 10 18 64 47.6 1 Center for the Promotion of Interdisciplinary Education and Research, Kyoto University 2
More informationStudies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth
Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,
More informationディスプレイと携帯端末間の通信を実現する映像媒介通信技術
Data Transfer Technology to Enable Communication between Displays and Smart Devices 倉木健介 中潟昌平 田中竜太 阿南泰三 あらまし Abstract Recently, the chance to see videos in various places has increased due to the speedup
More information(2008) JUMAN *1 (, 2000) google MeCab *2 KH coder TinyTextMiner KNP(, 2000) google cabocha(, 2001) JUMAN MeCab *1 *2 h
The Society for Economic Studies The University of Kitakyushu Working Paper Series No. 2011-12 (accepted in March 30, 2012) () (2009b) 19 (2003) 1980 PC 1990 (, 2009) (2001) (2004) KH coder (2009) TinyTextMiner
More information2017 (413812)
2017 (413812) Deep Learning ( NN) 2012 Google ASIC(Application Specific Integrated Circuit: IC) 10 ASIC Deep Learning TPU(Tensor Processing Unit) NN 12 20 30 Abstract Multi-layered neural network(nn) has
More informationIPSJ SIG Technical Report Vol.2009-DBS-149 No /11/ Bow-tie SCC Inter Keyword Navigation based on Degree-constrained Co-Occurrence Graph
1 2 1 Bow-tie SCC Inter Keyword Navigation based on Degree-constrained Co-Occurrence Graph Satoshi Shimada, 1 Tomohiro Fukuhara 2 and Tetsuji Satoh 1 We had proposed a navigation method that generates
More information,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
More informationWikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy
1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura
More information